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DaWaK 2024 : 26th International Conference on Big Data Analytics and Knowledge Discovery

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Conference Series : Data Warehousing and Knowledge Discovery
 
Link: https://www.dexa.org/dawak2024
 
When Aug 26, 2024 - Aug 28, 2024
Where Naples, Italy
Submission Deadline Apr 7, 2024
Notification Due May 10, 2024
Final Version Due Jun 15, 2024
Categories    big data   data mining   dat analytics   machine learning
 

Call For Papers

The 26th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2024) will be held in Naples, Italy on August 26-28, 2024.

All accepted conference papers will be published in a volume of "Lecture Notes in Computer Science" (LNCS) by Springer. LNCS volumes are indexed in Scopus; EI Engineering Index; Google Scholar; DBLP; etc. and submitted for indexing in the Conference Proceedings Citation Index (CPCI), part of Clarivate Analytics’ Web of Science. TOP papers, after further revisions, will be invited for publication in a SPECIAL ISSUE of DATA & KNOWLEDGE ENGINEERING (DKE) titled "Data Engineering, Data Analytics and Data Science".

SCOPE
DaWaK conference is a high-quality forum for researchers, practitioners and developers in the field of Big Data Analytics, in a broad sense. The objective is to explore, disseminate and exchange knowledge in this field through scientific and industry talks. The conference covers all aspects of DaWaK research and practice, including data lakes, database design (data warehouse design, ER modelling), big data management (tables + text + files), query languages (SQL and beyond), parallel systems technology (Spark, MapReduce, HDFS), theoretical foundations and applications, text and data mining techniques, and deep learning. The conference will bring together active researchers from the database systems, cloud computing, programming languages and data science communities worldwide.
Main topics include:

Theoretical models for extended data warehouses and big data
Conceptual model foundations for big data
Modelling diverse big data sources
Parallel processing
Parallel DBMS technology
Distributed system architectures
Scalability and parallelization using Map-Reduce, Spark, and related systems
Query languages
Query processing and optimization
Semantics for big data intelligence
Data warehouse and data lake architectures
Pre-processing and data cleaning
Integration of data warehousing, OLAP cubes, and data mining
Quantum technologies for data engineering
Polystore and multistore architectures
NoSQL storage systems
Cloud infrastructures for big data
Metadata for big data frameworks
Big data storage and indexing
Big data analytics: algorithms, techniques, and systems
Big data quality and provenance
Big data search and discovery
Big data management for mobile applications
Analytic workflows
Graph analytics
Analytics for unstructured, semi-structured, and structured data
Analytics for temporal, spatial, spatio-temporal, and mobile data
Analytics for data streams and sensor data
Real-time/right-time and event-based analytics
Privacy and security in analytics
Data visualization
Big data application deployment
Data science products
Novel applications of text mining for big data
Machine learning: auto AI, deep learning applications


SUBMISSION GUIDELINES
Authors are invited to submit original research contributions or experience reports in English. DaWak will accept submissions of both short and full papers.

* Short papers: up to 6 pages on preliminary work, vision papers or industrial applications
* Full papers: up to 15 pages (including references and appendixes). Full papers are expected to be more mature, contain more theory or present a survey (tutorial style) of some hot or not yet explored topics.

Papers exceeding the page limit or deviating from the formatting requirement are desk rejected.

Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition. Duplicate submissions are not allowed and will be rejected immediately without further reviewing.
Authors are expected to agree to the following terms: "I understand that the submission must not overlap substantially with any other paper that I am a co-author of or that is currently submitted elsewhere. Furthermore, previously published papers with any overlap are cited prominently in this submission."

Questions about this policy or how it applies to a specific paper should be directed to the PC Co-chairs

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